Neural Networks and Deep Learning

Date Created
13/10/2024

This course offers a comprehensive introduction to deep learning, covering neural network fundamentals, backpropagation, and optimization techniques. You'll explore CNNs for image processing, RNNs and LSTMs for sequence data, attention mechanisms, and transformers. Learn advanced topics like GANs, transfer learning, and model deployment, with a focus on ethical considerations and real-world applications.

What you will learn -

Introduction to Deep Learning and Its Applications
Fundamentals of Neural Network Architecture
Demystifying Forward Propagation and Activation Functions in Neural Networks
Understanding Backpropagation and Gradient Descent in Deep Learning
Vectorization
Mastering Hyperparameter Tuning
Regularization Methods for Preventing Overfitting in Deep Learning
Unveiling the Power of Adam and RMSprop
Unlocking the Power of Convolutional Neural Networks (CNNs) for Image Processing
Unraveling the Power of RNNs and LSTMs in Deep Learning
Unleashing the Power of Transfer Learning and Fine-tuning Pre-trained Models
Unveiling the Power of Attention Mechanisms and Transformers in Deep Learning
Unveiling the Power of Generative Adversarial Networks (GANs)
Navigating the Ethical Maze
Deploying Deep Learning Models in Real-world Applications